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  • 1. The Column Space of A Contains All Vectors Ax_2.vtt
  • 10. Survey of Difficulties with Ax = b.vtt
  • 11. Minimizing _x_ Subject to Ax = b.vtt
  • 12. Computing Eigenvalues and Singular Values.vtt
  • 13. Randomized Matrix Multiplication.vtt
  • 14. Low Rank Changes in A and Its Inverse.vtt
  • 15. Matrices A(t) Depending on t, Derivative = dA_dt.vtt
  • 16. Derivatives of Inverse and Singular Values.vtt
  • 17. Rapidly Decreasing Singular Values.vtt
  • 18. Counting Parameters in SVD, LU, QR, Saddle Points.vtt
  • 19. Saddle Points Continued, Maxmin Principle.vtt
  • 2. Multiplying and Factoring Matrices.vtt
  • 20. Definitions and Inequalities.vtt
  • 21. Minimizing a Function Step by Step.vtt
  • 22. Gradient Descent- Downhill to a Minimum.vtt
  • 23. Accelerating Gradient Descent (Use Momentum).vtt
  • 24. Linear Programming and Two-Person Games.vtt
  • 25. Stochastic Gradient Descent.vtt
  • 26. Structure of Neural Nets for Deep Learning.vtt
  • 27. Backpropagation- Find Partial Derivatives.vtt
  • 3. Orthonormal Columns in Q Give Q'Q = I.vtt
  • 30. Completing a Rank-One Matrix, Circulants!.vtt
  • 31. Eigenvectors of Circulant Matrices- Fourier Matrix.vtt
  • 32. ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule.vtt
  • 33. Neural Nets and the Learning Function.vtt
  • 34. Distance Matrices, Procrustes Problem.vtt
  • 35. Finding Clusters in Graphs.vtt
  • 36. Alan Edelman and Julia Language.vtt
  • 4. Eigenvalues and Eigenvectors.vtt
  • 5. Positive Definite and Semidefinite Matrices.vtt
  • 6. Singular Value Decomposition (SVD).vtt
  • 7. Eckart-Young- The Closest Rank k Matrix to A.vtt
  • 8. Norms of Vectors and Matrices.vtt
  • 9. Four Ways to Solve Least Squares Problems.vtt
  • An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,....vtt
  • Course Introduction of 18.065 by Professor Strang.vtt
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